LONG-TERM TRENDS IN THE SPATIAL STRUCTURE OF THE ECONOMY

Trends in changing of the economic space form under the influence of basic factors determined by the peculiarities and patterns of spatial development. In the new economic geography two groups of such factors are identified: among first nature factors are the availability of natural resources demanded by the market (mineral, land, etc.), advantageous geographical position (including a border position on global trade routes); second nature factors are the agglomeration effect and high population density yielding economies of scale; developed infrastructure that reduces economic distance; human capital (education, health, labor motivation, mobility and adaptability of the population); institutions contributing to improvement of the business climate, growth of population mobility, the dissemination of innovations, etc. (Krugman, 1993; Zubarevich, 2010).

The effect of these factors is extremely inertial, which determines the high degree of stability of spatial proportions. Thus, the inherited features of development (path dependence) represent the most important condition forming long-term trends in spatial development.

The modern spatial framework of the Russian economy was formed under the conditions of a planned economy. Plans were based on the principles of rational distribution of production common to all economies, such as accounting for the comparative advantages of regions, their rational specialization, minimizing production and transport costs, etc. However, economic criteria were not decisive in choosing the priorities of spatial development, since the country was solving political and geopolitical problems, including strengthening defense capability and develo** new territories and the economies of backward regions, including national republics. Even in cases where allocation was determined by economic criteria, these were the criteria of the almost completely closed economy of the Soviet Union, in which the Russian Federation was only part of a “single national economic complex”; costs and results were assessed by domestic prices, the structure of which was very different from global markets prices.

The breakdown of the planned system became a kind of “pure experiment,” when spatial development began to be determined by market factors and inherited features of development, since the central government, especially at the initial stage of reforms, did not have the resources to regulate spatial development; moreover, rejection of state regulation, in including spatial development, was one of the main principles of those reforms. As a result, Russia has 25 years of experience of a previously strictly planned regulated spatial structure of the Russian economy floating free in market conditions. Below, the article considers changes in the main spatial characteristics of the economy observed in the post-Soviet period and their correspondence (or discrepancy) with modern trends in spatial development emerging in the context of globalization of economies.

Directions of the Change in Spatial Proportions

Changes in the characteristics of the Russian economic space in the post-Soviet period are considered in a number of studies (Ershov, 2010; Mikheeva, 2008, 2016; Rossiiskie …, 2011); a common feature of the structural shifts noted in them is displacement of the population, labor resources, and production to the west and southwest.

Compared to the beginning of the 1990s, the shares of the Central, Southern, and North Caucasian federal districts (FD) increased in the spatial structure of the population, while the share of the Ural FD remained almost completely stable. In structure of GRP in 2017, compared to 1994, the first year for which official GRP statistics were published, the shares of the Central, Northwestern, Ural, Southern and North Caucasian federal districts increased; the shares of the Ural and North Caucasian federal districts increased in industrial production; the shares of the Central, Southern, and North Caucasian federal districts, in agriculture. Table 1Footnote 1 lists the changes in the spatial structure of the population, GRP, and industrial and agricultural production in the context of large macrozones.

Table 1.   Change in structure of indicators in context of macrozones, percentage points*
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The shift to the central part of the country (Center in Table 1) was determined by an increase in the share of the macrozone by 2017–2018 compared to the beginning of the period in the population, GRP, agriculture, and the main factor sha** the change in the structure was the growth in the share of Moscow and Moscow oblast. The increase in the Center’s share in the GRP amounted to 9.5 percentage points; part of this increase was due to recording of income in Moscow from the export of natural resources; this has been repeatedly noted in the literature, but the fact of an increase in the real contribution to the total GRP of the Moscow urban agglomeration is also obvious. Similar trends were characteristic of development of the southern macrozone, the share of which in population, total GRP and agriculture, also increased. The share in these indicators for each of the other macrozones decreased at the end of the period compared to its beginning.

The trends in the change in structure of industrial production proved the opposite; the shares of the Center and the South in the all-Russian indicator decreased, and a shift in the spatial structure of industrial production took place towards the Northwest, Urals, and the Volga region. The increased share of these macrozones in industrial production did not lead to an increase in their shares in the total gross regional product (GRP). The share of eastern regions by 2017–2018 decreased compared to the beginning of the period for all indicators, with the most significant drop in the share of eastern regions in GRP.

In reality, the pattern of spatial changes is not as trivial and homogeneous; in different periods, different trends were found for different indicators.

Among the most stable spatial structures is distribution of the population across the country. Cardinal changes in the distribution of the population occurred in the early 1990s; current trends are formed against a relatively stable population of the country as a whole (except for population growth in 2014 due to inclusion of the Republic of Crimea and Sevastopol into the Russian Federation)Footnote 2 and are determined by the demographic the situation—which is unfavorable in the vast majority of regions—and interregional migration processes, the intensity of which is low.

After the start of economic reforms, while Russia’s population as a whole decreased, it increased in only two macrozones: the Center and the South. The population decreased most intensively in the Far Eastern and Northwestern federal districts. In the top three districts with the most significant depopulation in absolute terms, in addition to the traditional Far Eastern and Siberian federal districts, there was the Volga FD (a decrease of more than 2.3 million people), located in the center of the country and having quite favorable natural conditions. Negative and demographic and migration factors both contributed to population reduction in the Volga region.

The spatial structure of the population in the context of large macroregions (federal districts), due to their size, changed to a much smaller extent than what occurred at the level of the federal subjects.

In the favorable economic situation of 2000–2008, Russia’s population decreased by 3.6%, while the dynamics remained positive only in the North Caucasian and Central federal districts. The downward trend in Russia’s population was reversed in 2009, followed by low, but all the same, increase. However, in general, for 2001–2010, the decrease in population even exceeded that of the previous decade.

By 2017, Russia’s population increased by 1647 thousand people compared to 2010 (excluding the annexation of Crimea). The majority of increase also fell on the North Caucasian and Central federal districts, while population decrease continued only in the Volga and Far Eastern federal districts. A new wave of population decrease began in 2018, the number fell by 11.8 thousand people compared to the previous year, the largest drop in the number in absolute terms was observed in the Volga and Siberian federal districts, in percentage terms—in the Volga and Far Eastern federal districts. The tendency of a consistent decrease in the share of the eastern territories of the country in the total population proved stable; in 1990, 28.3% of the country’s population lived in the three eastern districts; in 2000, 27.1%; in 2018, 26.1%.

Changes in the territorial structure of the population are formed under the influence of two processes—natural population increase and migration. Natural increase until 2009 was negative in Russia as a whole and in all federal districts, except for the North Caucasian. Since 2009, a positive natural increase has been recorded in the Ural FD, where it was provided at the expense of the Khanty-Mansi and Yamalo-Nenets autonomous okrugs and Tyumen oblast. Natural population increase in these regions was positive during the 2000s; however, it was not enough to cover the population decrease in the Southern Ural regions and ensure a positive population trend in the Ural FD as a whole.

Steady positive natural population increase since 2011 has been observed in the Siberian FD, where it is provided not only by the ethnic republics of Tyva, Altai, Buryatia, Khakassia, but also by large industrial centers: Krasnoyarsk krai and Irkutsk and Tomsk oblasts. In 2012, for the first time in many years, the number of births exceeded the number of deaths in the Far Eastern FD and a positive result was achieved due to a natural increase in the Republic of Sakha (Yakutia) and Khabarovsk krai, and a positive natural increase in Chukotka Autonomous Okrug.

The emerging positive demographic trends of 2012–2016 were changed by the situation of 2017–2018. Positive natural population increase was preserved only in the North Caucasian and Ural federal districts (at the expense of Tyumen oblast and autonomous okrugs). In all other districts, there was a natural population decrease; the positive natural population increase in the ethnic republics of Siberia and the Far East, as well as the republics of Tatarstan and Kalmykia and the Nenets Autonomous Okrug was not enough for a positive dynamics for each federal district as a whole.

The continuing downward trend in the share of Russia’s eastern regions in the total population is determined by both natural population decrease and migration. The spatial structure of migration flows, considered in the context of federal districts, changes little in the dynamics; the Central, Northwestern, and Southern federal districts are characterized by a steadily migration increase, while the Volga, Siberian, Far Eastern, and, since 2010, the North Caucasian federal districts are characterized by a steadily decrease (Table 2).

Table 2.   Migration increase rates (per 10 000 people)*
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The magnitude of migration flows significantly depends on the economic situation in the country. The 2009 crisis was marked by a significant reduction in migration flows in 2010; in 2011–2013, the number of migrants increased, but migration flows have not reached the level of 2007–2009. In 2015–2016, against the deteriorating economic situation, the absolute value of the net migration in Russia as a whole decreased; however, for individual federal districts (Volga, Siberian), migration outflow increased.

After the 2008–2009 crisis, there were stable migration flow’s ratios in the federal districts: a positive inflow in the Central, Northwestern, and Southern federal districts, a minimal but positive one in the Ural FD, and a steady migration outflow in all other federal districts; the once positive migration population increase in the North Caucasus not only changed direction, but in terms of its negative values, approached those of the Far East.

A characteristic feature of the migration flows of the early 2000s was predominant migration within the federal districts, and this trend has continued, but the share of such migrations is gradually decreasing. The Siberian FD has the highest rates of intradistrict migration, where in 2000 82.6% of migrations were moving between Siberian regions; in 2017, the figure dropped to 76.5%. The second largest intradistrict migration is the Central FD, in which the corresponding migration rates were 80.1 and 75.8%.

In general, over the period under review, the share of intradistrict migration decreased in all districts, except for the Far East, which can be regarded as a trend towards an increase in interdistrict mobility of the population.

During the entire post-Soviet period, the population was concentrated in the largest regions in terms of population size and economic potential. After the 2008–2009 crisis, the process of concentration slowed, the share in the Russian population increased only for Moscow and St. Petersburg. The list of largest federal subjects in terms of population size is stable; the relative positions of the regions have changed over the years, but after 2000, the list of leaders remained unchanged (Table 3).

Table 3.   Ten largest federal subjects in terms of population size in Russia and corresponding federal district (regions are ranked by population size in 2018), %
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The share of top ten regions by population size in the country increased from 34.6% in 2000 to 36.3% in 2010 and 37.6% in 2018. As well, population growth in the ten largest regions exceeded the total growth in Russia; i.e., it exceeded the population decrease in all other regions. However, even in the top ten regions, the population dynamics were positive throughout the entire period of 2000–2018 only in Moscow, Moscow oblast, Krasnodar krai, and St. Petersburg.

In other, smaller regions, population growth in 2018 compared to 2000 took place in Belgorod, Leningrad, Astrakhan, Novosibirsk, Tomsk, Kaliningrad, Tyumen oblasts; the Nenets, Khanty-Mansi, and Yamalo-Nenets autonomous okrugs; and the republics of Altai, Tyva, and all regions of the North Caucasus, except for Kabardino-Balkaria. All other federal subjects, including the largest ones, were losing population. The maximum reduction in the population over the period (386.7 thousand people) took place in the Nizhny Novgorod oblast, which, nevertheless, remained in the top ten largest federal subjects.

Analysis of the population dynamics after 2000, when the market transformation processes can be considered completed, shows that the directions of spatial shifts coincide with the global trends noted in the literature: concentration of the population in the richest regions. This is confirmed by the ratio of population growth rates and per capita GRP, taken with account for interregional price differentiation,Footnote 3 which reveal a trend of faster population growth in the richest regions in terms of per capita GRP (see Fig. 1a).

Fig. 1.
figure 1

Increase in population of federal subjects in 2018 compared to 2000 and ratio of average per capita GRP, taking into account FBGS in 2017, to average for Russian Federation: (a) all regions, (b) without rich regions.

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The main contribution to the differentiation of per capita GRP indicators comes from oil and gas producing regions: the Nenets, Yamalo-Nenets, Khanty-Mansi autonomous okrugs and Moscow, which have the highest per capita GRP in the country and a positive population growth, as well as Sakhalin oblast, which has taken a leading position in recent years in terms of per capita GRP, but has a migration decrease. These regions have a significant influence on the formation of this trend, and their exclusion from the analysis demonstrates a U-shaped trend (see Fig. 1b).

The largest population growth was observed in the poorest regions; here the main factor is natural growth, then as we move towards the average Russian level of per capita GRP, the growth decreases, and in regions with a per capita GRP level exceeding the national average, population growth rates increase, and in this case migration growth plays a significant role, although this process is not the same in all regions.

Excluding the very specific factor of population growth in the northern oil and gas producing regions, the general direction of shifts in the spatial distribution of the population remains—from the northeast to the south and west to the regions most favorable for the life of the population of the Central, Southern, and Northwestern federal districts; this is mainly due to migration, since poor regions are characterized by positive natural population growth. The general trend formation of population growth in rich regions mainly due to external migration, and in poor regions, due to natural population growth. The total migration population growth for 2000–2018 was positive in 31 federal subjects, although the number of regions in which growth was consistently positive is slightly more than ten.

Comparison of the direction and intensity of migration flows for different time periods shows that their spatial structure depends on the economic situation. In 2000–2008, the main flow of migrants was directed towards the Central FD (Moscow oblast, Moscow, Belgorod, Voronezh, and Lipetsk oblasts; in some years, a migration increase was observed in Kaluga and Tula oblasts) and towards the Southern FD (Krasnodar krai and Rostov oblast). A significantly smaller increase in migration occurred in the Northwestern FD (St. Petersburg, Leningrad and Kaliningrad oblasts); a positive migration increase in the North Caucasus occurred at the expense of Stavropol krai.

In the eastern regions, the migration balance was negative; the Siberian FD and the Far East were the leaders in the absolute number of migrants. In the Siberian FD, the migration balance was negative in all federal subjects, with the only exception in some years being the Novosibirsk and Tomsk oblasts. In the Far East, as a result of migration, the population was lost by all federal subjects. In the Ural FD, as a rule, the Khanty-Mansi Autonomous Okrug and Tyumen oblast have migration increase, but even these regions lost population due to migration in 2004–2006.

The migration situation is very heterogeneous in the Volga FD, where the migration outflow is relatively small, and in 2007–2009 for the federal district as a whole, the migration balance was also small (2–5 per 10 000 people), but positive. The situation for the district as a whole is determined by the ratio between migration inflow to large industrial centers (the Republic of Tatarstan, Nizhny Novgorod and Samara oblasts) and migration outflow from all other federal subjects.

In 2009–2010, due to the crisis, migration flows decreased almost everywhere, and after the crisis, their structure changed. The share of migration to the Central and Southern federal districts has decreased, but the share of migration flow to the Northwestern FD has significantly increased. The Ural FD was included in the federal districts with positive migration. The North Caucasian FD was among the federal districts that lost population due to migration. In the Siberian and Far Eastern federal districts, the absolute indicators of population outflow decreased, and the share of the Volga FD in total migration flow increased. The general direction of migration flows—towards metropolitan areasFootnote 4 and large industrial centers—has not changed, although the intensity has decreased.

By 2018, compared to 2000, the migration activity indicators decreased; in 2000, the migration inflow rates were in the hundreds (–704 per 10 000 people in Chukotka Autonomous Okrug, –384 in Magadan oblast, +239 in Moscow); in 2018, the maximum migration inflow (+239) was in Leningrad oblast, and the maximum outflow (−187), in Magadan oblast.

The differences also relate to changes in the direction of migration flows. In 2000, there was no relationship between the migration growth rate and level of average per capita income, and with the exception of regions with the highest incomes and positive growth (Chukotka Autonomous Okrug, Moscow, Tyumen oblast with the autonomous okrugs), it became negative (Fig. 2a). In 2017, the situation was reversed. The migration growth coefficients increase along with an increase in household income (Fig. 2b) (here, as in the previous graph, regions with the highest incomes are excluded: the Chukotka Autonomous Okrug, Moscow, and Sakhalin oblast); thus, personal income level began to significantly determine migration flows.

Fig. 2.
figure 2

Relationship between migration increase rate in federal subjects and ratio of cash income per capita (CI), taking into account FBGS, to average for Russia in 2000 (a) and 2017 (b).

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The nature of migration processes is increasingly affected by economic factors that determine migration. Regions with high economic potential in which large cities are located attract migrants; in all federal districts, there is migration outflow from peripheral regions with a lower standard of living. This gives grounds to believe that the established directions of migration flows will continue in the future.

In general, the changes in the spatial proportions of production and distribution of resources in the Russian Federation that occurred after the collapse of the USSR indicate that the settlement pattern and production structure proved quite stable, and the most stable is the distribution of population and employment, which governs the inertia of the territorial structure of industries related to servicing the population. Market transformations, uneven economic growth, and the economic crisis have not been able to significantly “rock” the spatial framework of the Russian economy when considered in the context of large macroregions. This means that the existing system of spatial distribution of the population and economic activity, not only now, but in a fairly long term, will determine not only the limitations, but also the vectors of the country’s development as a whole.

Specialization or Diversification of Regional Economies?

Economic reforms led to deindustrialization of the economies of many regions, as well as simplification and impoverishment of their production structure. Change in the specialization of Russian regions in the direction of its growth or decline (diversification of production) is one of the essential characteristics of the internal structure of the economic space. Diversification of the structure of production is seen as a strategic direction widely declared in official documents and scientific publications as an innovative path to development of the Russian economy and is assumed as a basic development strategy for almost all Russian regions. The advantage of diversified economies is that they are less sensitive to fluctuations in the dynamics of individual industries, including price shocks, since a decline in one industry can be offset by growth in another, which ensures stability of the economy as a whole (Duzhenko, 2015; Economic …, 2014; Hvidt, 2013; Iogman, 2008; Mikheeva, 2017). A diversified economy provides greater opportunities for creating efficient employment, not only in terms of smoothing out shocks in individual sectors, but also creating a more flexible labor market in a region due to the cross-sectoral flow of labor. An argument in favor of diversification of the production structure is greater resistance of regions to the influence of externalities, including external demand, since in a diversified structure, the generation of internal demand is ensured by intersectoral relations.

In addition, no one refutes the theory of comparative advantage as one of the fundamental principles of regional development, according to which specialization of regions is one of the most important conditions for national growth. The theoretical aspects of integrated development of regions, which implies the formation of a regional structure of production based on national economic specialization of regions, combining optimal levels of specialization and diversification of production, have been worked out in detail in Soviet literature and remain relevant. Theoretically, the optimal level of diversification of a particular region from the viewpoint of economic criteria can be determined by an interregional intersectoral approach that takes into account a combination of national economic and regional factors and limitations, as well as the competitiveness of the region and its products in the domestic and foreign markets. The tools for carrying out such calculations are, e.g., interregional models (Granberg et al., 2007); the possibilities and limitations of using such models are widely known.

In practice, the choice of the optimal combination of diversification and specialization strategies in regions is extremely difficult due to the complexity and multidimensionality of the problem itself, the difficulties of formalizing strategic alternatives, information problems, and the lack of generally recognized measures and criteria for diversifying the regional economy. Below, an attempt is made to quantify the trends in diversification (specialization) of production in Russian regions as a factor determining complication (or simplification) of the structure of production in regions, respectively, and the internal structure of the economic space.

Assessment methods and information base. The literature offers a wide range of indicators that can be used to trace the dynamics of differentiation of the sectoral structure of production for a particular economy or to compare the degree of diversification for different economies. A detailed review of quantitative methods for determining the level of diversification with a description of the advantages and disadvantages of each indicator is given in (Measuring …, 2008); as applied to analyzing diversification of the economy of Russian regions, in (Mikheeva, 2013).

Common indicators of economic diversification include the entropy coefficient and the Herfindahl–Hirschman coefficient. The first compares the current structure of the economy with an equally proportional distribution; a higher value of the coefficient indicates a higher level of diversification, while a lower value indicates specialization of the economy. The maximum coefficient is achieved with a uniform distribution, when the shares of all sectors are equal; the minimum value of the entropy coefficient, equal to zero, is achieved in the case of maximum specialization, when all activity is concentrated in one sector.

The Herfindahl–Hirschman coefficient shows the degree of dominance of individual sectors of the economy in a region; it is calculated as the sum of the squared values of the regional market shares occupied by each sector. The coefficient varies from zero (when the economy is represented by many sectors that occupy an insignificant share in the overall regional indicator) to one (when the entire economy is fully specialized and represented by one sector); thus, a low Herfindahl–Hirschman coefficient means a decrease in the degree of specialization of the economy and, accordingly, an increase in its diversification; conversely, an increase in the value of the coefficient means an increase in the dominance of one of the sectors and an increase in specialization.

The absolute values of both coefficients depend on the number of sectors under consideration. Like any other indices calculated using one statistical indicator, both of these coefficients do not reflect the total diversity of such a phenomenon as diversification; the simplicity of their calculation is the main advantage.

For interregional comparisons of the level of diversification of production and comparison of the indicator of a particular region with a certain benchmark (for the latter, as a rule, the national economy is considered), the Hachman coefficient is used, which is based on the coefficient of localization of production (Measuring …, 2008; Moore, 2001; OECD, 2006). The Hachman coefficient shows how close the regional structure of the indicator under consideration is to the structure of the corresponding indicator for the national economy. The value of the coefficient ranges from 0 to 1; 1 means that the structure of the indicator for the region completely coincides with the national one, while 0 means that the structures are completely different.

The entropy and Herfindahl–Hirschman coefficients do not take into account the scale of the economic system. In contrast, the Hachman coefficient measures the level of diversification and the scale of the estimated region with a certain standard; therefore, it is the most widely used in studies on this topic.

In foreign studies, the main indicator by which regional structures are analyzed and the characteristics of diversification processes are plotted, as a rule, is employment (Esteban, 2000; Knudsen, 2000; Moore, 2001). The advantage of this indicator is that it quite accurately characterizes the general economic dynamics and does not depend on cost indicators. Due to the specifics of the Russian labor market, low interregional and interindustry mobility of labor resources, and significant hidden employment, employment indicators do not reflect real economic dynamics and output growth is not necessarily accompanied by an increase in employment, just as a decline in production often occurs without a reduction in employment.

The possibilities of analyzing long-term changes in the sectoral structure of employment and production are limited to the existing statistical base. The transition to publication of data since 2017 in All-Russian Classifier of types of economic activities (OKVED-2) ensures comparability of time series with data up to 2017, despite the fact that the sectoral structure of gross value added (GVA) in the context of 15 types of economic activities (TEA)Footnote 5 published in OKVED-2007 for 2004–2016, and the GVA indices in regions by TEA are presented here for 2005–2016; they were used for calculations based on the GVA structure in comparable prices.Footnote 6 The structure of employment in a comparable format was published for 2000 and 2005–2016, represented by 13 aggregated TEA in OKVED- 2007.Footnote 7

For a high level of aggregation, regional employment structures are close and change little over time. About 30–35% of those employed in regions are in the social services industry; about 15-20%, in trade; the share of these TEA in the employment structure for most regions is approximately the same—regional differences in the employment structure pertain mainly to the share of agriculture and the mining and manufacturing industries.

Another indicator used to characterize structural shifts is income, or GVA. The problems of its use in the general case are related to the choice of price metrics that ensure data comparability. There are also specific problems in accounting for the created value added in regions because the income created is not always recorded at the place of their production. There are other problems of correctly accounting for the value added created in regions (Mikheeva, 2013); however, there is no real alternative to the GVA indicator when conducting regional calculations.

As in the case of employment, the aggregate GVA structures in regions are quite close (with the exception of a small number of regions) and change little over time.

More indicative is analysis of structural shifts for industrial production, since the regions differ quite strongly in the structure of industry. The available calculation base is represented by data on the volume of shipped goods and services and structure of mining, manufacturing, production and distribution of electricity, gas and water at current prices.

Calculations on the structure of industrial production were carried out for 14 TEA,Footnote 8 where mining is represented by two positions; processing by 11; production and distribution of electricity, gas, and water, by 1. Calculations for industry were carried out at current prices, so their results are affected by the influence of interregional price differentiation, but it cannot be leveled on the basis of available statistics.

Level of diversification of the regional economy. The structure of value-added production for the economy as a whole, even when a detailed product range is used (68 TEA)Footnote 9 is stable; the coefficient of relative structural shifts for 2003–2016 was 0.260. To assess the level of diversification of the Russian economy, the entropy and Herfindahl–Hirschman coefficients were used, calculated on the basis of employment and GVA (Table 4).

Table 4.   Indicators of level of diversification of Russian economy (2003–2011, in constant 2008 prices; 2011*–2016, in 2011 prices)
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The maximum value of the entropy index is determined as a function of the number of sectors included in the calculation. In the case of 68 TEA, it is 4.220; in the case of 15 TEA, 2.708. The estimates given in the table show, first, a fairly high level of diversification, which is natural for a large economy; it is 82% of the maximum possible for calculation by categorized nomenclature and 88% by aggregated; second, close estimates for calculation of the structures of employment and GVA in comparable prices; and third, estimates for the period under review are robust. If the changes in coefficients that have taken place, which amounted to ~1–2%, are considered significant, then the level of diversification of the economy in 2015–2016 is slightly less than at the beginning of the period.

Diversification of the employment structure in Russian regions when estimating the entropy coefficient for aggregate indicators, in fact, comes down to a more even distribution of employment between sectors, and when using the Hachman coefficient, to bringing the regional employment structure closer to the employment structure in the national economy, which seems diversified.

According to aggregated data on employment in the economies of the federal subjects for 2000, Siberian regions were the most diversified: Krasnoyarsk krai; Irkutsk, Tomsk, and Novosibirsk oblasts; in addition to these are Leningrad oblast, Perm krai, and the Republic of Tatarstan. In 2016, Krasnoyarsk krai, Irkutsk and Tomsk oblasts, and the republics of Tatarstan and Bashkortostan remained in the first positions; the level of employment diversification increased significantly in Saratov and Omsk oblasts.

The most specialized at the beginning and at end of the period are the northern raw materials regions: Khanty-Mansi, Yamalo-Nenets, Nenets autonomous okrugs, Magadan oblast, and the Republic of Sakha (Yakutia). However, in 2016, among the regions with a high level of specialization was Moscow with a high share of those employed in the service industries, including trade, as well as the republics of Dagestan, Altai, and Kalmykia with a very specific specialization in social services, which was a direct consequence of the growth in income in public sectors in these regions.

In 2016, compared to 2000, the Hachman coefficient increased in 51 regions; i.e., the economy of more than half the regions has become more diversified. The level of diversification has changed even for the federal districts, with the indicator increasing in all districts, except for the Central, Southern, and Ural federal districts, where the economy has become more specialized.

Comparison of the increased level of diversification in employment (based on the Hachman coefficient) with the dynamics of GRP reveals a weak negative dependence. GRP growth rates for the period under review are slightly higher in highly specialized regions than in diversified ones. The correlation coefficient for the Hachman coefficient in 2016 and employment growth rate for the entire period is ‒0.240, but the dependence is weak and negative. Similar results were obtained for a number of regions in the United States, where the relationship between the level of diversification and employment growth is negative and employment in specialized regions is growing faster than in diversified ones (Measuring …, 2008).

The Hachman coefficient for the aggregate GRP structure was calculated in comparable and current prices; the GVA structure in comparable prices was calculated from data on production indices for TEA. The list of regions that can be classified as the most diversified and most specialized is quite close, although the ranks of the regions differ. In 2004, the economies of Perm krai; Kaliningrad, Volgograd, and Samara oblasts; the republics of Karelia and Udmurtia were classified as the most diversified in terms of GRP structure.

In 2016, the maximum Hachman coefficients were achieved in the Samara, Irkutsk, and Kaliningrad oblasts; the republics of Udmurtia and Karelia; and Perm krai; there were no significant changes in the relative positions of the regions. According to the Hachman coefficient, the level of economic diversification, calculated at current prices, increased in 2016 compared to 2004 in 39 federal subjects and decreased in 43.

Calculation of the Hachman coefficient for the GRP structure does not reveal a significant relationship between changes in the level of production diversification and economic growth rates. The correlation coefficients for the change in the Hachman coefficient, the growth rates of GRP and employment are 0.142 for employment and –0.088 for GRP. In general, the growth rates of production and employment are higher in specialized regions, although a high level of specialization, which is typical, e.g., for Magadan oblast and Kamchatka krai, is not associated with high GRP growth rates. The lack of dependence between the change in the level of diversification and the rate of economic growth can be explained by the stability of the structure of production and employment in the period under review. The structure of GRP and employment in the context of 13 TEA in regions is quite close due to the high level of aggregation of TEA. Interregional differences in the level of diversification are more significant for industrial production, the structure of which is more diverse in regions.

The most diversified industrial regions in 2016 included the Republic of Tatarstan; Perm krai; Moscow; and Saratov, Samara, Nizhny Novgorod oblasts, while the level of diversification in 2016 compared to 2005 in all these regions increased.

The industry of the Chukotka Autonomous Okrug; Magadan, Ivanovo, Amur, and Arkhangelsk oblasts; and the Republic of Sakha (Yakutia) proved the least diversified (and, accordingly, more specialized): in all these regions, except for Yakutia, the Hachman coefficient in 2016 was lower than in 2005 and the level of diversification thus assessed declined.

The relationship between the growth rate of industry and the level of its diversification can be traced more clearly than for the aggregate indicators of employment and GRP. The aggregate index of industrial production over the past decade for diversified regions is close to the all-Russian trend; regions with a high level of specialization are characterized by significantly larger deviations, both positive and negative. In general, the trend towards higher rates of industrial production predominates for regions in which the level of specialization has increased; the correlation coefficient for the rates of economic growth and increase in the level of diversification is negative: –0.272.

The above estimates for the levels of diversification of the economy as a whole and industry confirm the hypothesis that diversification leads to more stable growth of industry and the economy of a region, but it does not ensure high growth rates. A breakthrough is driving increased specialization, as shown by the regions that achieved the highest growth rates over the past decade: Kaluga, Kaliningrad, and Sakhalin oblasts. In addition, the growth in specialization inevitably causes risks to a region: e.g., a decrease in the level of diversification in a number of regions (Oryol and Orenburg oblasts, the Republic of Ingushetia) was accompanied by low rates of production growth.

Analysis of structural shifts in employment, GVA, and industrial production shows that the structure of these indicators in specific regions has changed little; in most regions, it still corresponds to the structure of 2000–2004. The past ten-year period, including the years of intensive economic growth, was lost for most regions in terms of modernizing the structure of production and employment.

The formally estimated coefficient of diversification of the Russian economy is very stable; it hardly changed at all over the analyzed period. Trends in the level of diversification for individual regions are multidirectional: in about half the regions, diversification increased, while in the other half, it decreased.

The relationship between the level of diversification of production and the rate of its growth is most typical of industrial production: the higher the specialization of production in regions, the higher the rate of industrial production, although in some highly specialized regions, the growth rate is low. This means that the growth rate of production is governed by a more complex set of factors than the structure of production, but diversification of the economy ensures its stable growth due to concentration of resources in the most competitive areas, although in this case, certain risks remain.

Interregional differentiation of average per capita GRP and household income. Differentiation of the indicators of socioeconomic development of regions is one of the most important characteristics of space. Smoothing of interregional differences is one of the important tasks of regional economic policy, which is reflected in many publications. A detailed analysis of such publications is given in studies by K.P. Gluschenko (2010, 2011). As a rule, neoclassical models of economic growth are used as a theoretical basis for studying differentiation processes. Most publications, following (Barro et al., 2004), test the hypothesis of convergence or divergence of regional indicators, and it is stereotypically assumed that in the long term, per capita GDP indicators for national economies converge. However, Gluschenko demonstrated that neoclassical models do not yield unambiguous conclusions on convergence of economic growth.

Such models predict income convergence only in the case when the economies under consideration are homogeneous in terms of the production functions used in the model constructions. Moreover, neoclassical models (Rohmer’s model) show that the levels of per capita output do not have to converge: growth in less developed countries may be consistently lower than growth in highly developed countries, or even absent. Taking into account the open nature of the economy in regions, a number of additional conditions appear, due to which the rate of convergence may increase: this is the relatively free flow of labor, or convergence of economies within separate clusters that unite economies with relatively homogeneous conditions.

Currently, there are no theoretical works substantiating the inevitable convergence or divergence in the development of regions. The dynamics of interregional differentiation is measured using different statistical indicators, among which the most common are per capita GRP and household income. Various quantitative indicators are used to measure differentiation (Anand, 1983; Kataoka, 2019; Malkina, 2015), and different indicators are used (e.g., the ratio of the standard deviation to the average value of the indicator) and the range of fluctuations (the ratio of the maximum value of the indicator to the minimum).

For inertial regional systems, trends in interregional differences can be revealed only over a sufficiently long time interval. Of course, the ability to assess the dynamics and extent of interregional differentiation is limited by the available statistical base. Data on the volume of GRP for federal subjects have been published by Rosstat since 1994, and for autonomous okrugs within individual federal subjects, since 2000. Statistics on average per capita monetary household income by region have been published since 1995, and for autonomous okrugs, since 1997. The most convenient indicator, the FBGS, published since 2001, is the most convenient for taking into account interregional price differentiation. In this chapter, the subsistence minimum for calculations for 1994–2000; since 2001, the FBGS. To adjust the average per capita cash income, the subsistence minimum for the entire period of 1995–2018 was used.

Another statistical problem is accounting for data on autonomous okrugs. Until 2010, Rosstat did not calculate average per capita indicators for districts, arguing that there were strong discrepancies between the indicators of the permanent and present population in districts due to the difficulties in accounting for shift workers. The inclusion of autonomous okrugs—independent federal subjects—in the calculations increases the range of fluctuations and differentiation of indicators, but it does not change the trend. The influence of the Moscow urban agglomeration, which makes a significant contribution to interregional differentiation, is similar; when it is excluded, the absolute value of the variation decreases, but the trend persists.

Below the results of calculations carried out for 79 federal subjects (including the autonomous okrugs of the respective federal subject) are presented; the Chechen Republic, as well as the Republic of Crimea and Sevastopol, was not taken into account.

Over the entire period under review, the ratio of the maximum and minimum levels of per capita GRP, adjusted for the value of interregional price differentiation, changed significantly: the gap was 18.8 times in 2004 and 12.4 times in 2017, and the maximum gap was 28.8 times in 2005 (Fig. 3). A significant increase in differentiation was noted during the period of economic growth from 1999 to 2006.

Fig. 3.
figure 3

Indicators of interregional differentiation of per capita GRP: max/min is ratio of maximum to minimum indicator (times), var79 is variation coefficient for 79 regions (right axis), var77 is variation coefficient with exclusion of Tyumen and Sakhalin oblasts (right axis).

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The variation coefficient for the average per capita GRP in 1994 was 0.461, and in 2017, it was 0.493. This indicator decreased until 1998; in 2003–2005, it slightly increased, while after 2005, it remained almost unchanged; a slight increase occurred in 2010 and 2011 as the economy recovered from the crisis, as well as in the crisis-related conditions of 2014–2017. Thus, the indicators of interregional differentiation increase, as a rule, in conditions of economic growth and decrease in periods of decline in production. This is explained by the current growth model of the Russian economy, when export-oriented rich regions are the most sensitive to deterioration of the economic situation; these determine the high degree of differentiation in GRP indicators.

A more detailed analysis shows that for most regions, the differences in per capita indicators are much smaller than follows from assessments of the differentiation of the entire group of regions. Oil and gas producing regions make significant contribution to the dynamics and magnitude of interregional differences. Exclusion from the calculation of Tyumen and Sakhalin oblasts, which have the maximum levels of per capita GRP, reduces the value of the interregional variation of the indicator by almost a third. The dynamics of production and per capita indicators in the oil and gas producing regions also explains the increase in the variation indicators in years of crises. With the exception of Tyumen and Sakhalin oblasts, an increase in variation in 2014–2017 did not occur.

Interregional differences in the average per capita household income, adjusted for the subsistence minimum, are much smaller than the differentiation in the production indicators (Fig. 4). During this period, there was a decrease in the level of interregional differentiation of average per capita household income for both differentiation indicators.

Fig. 4.
figure 4

Indicators of interregional differentiation of average per capita household income: max/min is ratio of maximum to minimum indicator (times), var79 is variation coefficient for 79 regions (right axis), var78 is variation coefficient with exclusion of Moscow (right axis).

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The gap between maximum and minimum per capita income decreased from 11.1 in 1995 to 2.7 times in 2018. The variation coefficient decreased from 0.324 in 1995 to 0.231 in 2018. Income differentiation indicators grew until 2000, when the gap in household income between regions was maximum, then there is a downward trend in differentiation, although in some years (2004, 2007, 2009), there was a slight increase in the variation coefficient.

The most important factor in the interregional differentiation of household income is associated with Moscow. In 1995, the average per capita cash income (taking into account their adjustment for the difference in prices) in Moscow exceeded the national average by 3.3 times; the maximum gap was noted in 1999, 3.6 times, then the gap gradually decreased, and in 2018 it was 1.9 times. The influence of the Moscow factor exhausted itself after 2007, when exclusion of Moscow from calculation of variation in per capita income ceased to influence the differentiation indicators.

In general, during the period under review, a reduction in interregional differences took place for the indicators of average per capita production (GRP per capita) and average per capita incomes. A downward trend in differences in per capita income has been observed since 2001. Interregional differences in per capita production decreased in the period up to 2002; after their slight increase in 2003–2005, the indicators of interregional differentiation stabilized and have remained almost unchanged.

Smoothing of interregional differences in average per capita GRP and cash income is considered in the literature as an indicator of improvement in the quality of the economic space. In most countries, this leveling of interregional differences is a regional policy goal. Russia is characterized by poorly efficient regional policy (Aganbegyan et al., 2013; Fundamental’nye …, 2013), so it can be suggested that the decrease in interregional differentiation of per capita GRP and cash income occurs regardless of the ongoing regional policy. The specific feature of the downward trend in interregional differentiation is that differentiation indicators, as a rule, grow in a favorable economic environment and decrease when overall economic growth slows; i.e., a reduction of interregional differences occurs due to worsening of the position of rich regions, rather than the poor ones being pulled up to the level of the rich.

Is the Spatial Structure Becoming More Efficient?

The problem of determining the effectiveness of a spatial structure, like any other socioeconomic process, is extremely complex and multifaceted. In the most general view, this is the cost/benefit ratio. It is possible to talk about the economic, socioeconomic, social, sociopolitical effectiveness of the spatial structure, commensurate with the economic, sociopolitical results, and costs of achieving them. From the viewpoint of certainty and quantitative expression of these concepts, it is probably simplest to talk about economic effectiveness, although in this case, the definition of costs and benefits is a very difficult task. Strictly speaking, it is incorrect to compare the effectiveness of the spatial structure of the Russian Federation in the Soviet and post-Soviet periods, since these are different types of economies aimed at different end results, especially since the set of statistical data for correct comparisons is very limited. However, at the qualitative level, such comparisons are appropriate and they have been made in a large number of post-Soviet publications with peremptory statements about the higher efficiency of the market spatial distribution of factors and production results compared to a planned economy.

In qualitatively assessing the results of planned placement of productive forces, it should be noted that it ensured not only solution of the sociopolitical problems facing the country, but problems of spatial development proper: convergence of levels of economic development and employment of labor resources in different regions, achievement of a comparable level of social services, and even convergence of people’s income level (Mikheeva, 2016).

One can logically suggest that changes in the spatial structure under the influence of market mechanisms, including rehabilitation of the regional economy during crises, as a result of which the positions of the most competitive regions should improve, are aimed at increasing the effectiveness of the economy as a whole. It is difficult to test this hypothesis statistically, since there both methodological and informational problems exist; therefore, let us consider certain aspects of predominantly qualitative assessments.

Assessment methodology. The economic effect of a change in the production placement system can be assessed using macroeconomic indicators, which, on the one hand, reflect all changes in the structure of production due to the emergence of new industries in a region, modernization of old ones, and an increase in production volumes due to this, and on the other, which take into account many other factors not directly related to the spatial distribution of production. However, when using macroindicators, it is impossible to isolate the direct impact on the regional dynamics caused precisely by changes in the location of production.

Without pretending to a generalized assessment, let us consider certain aspects of determining the effectiveness of the spatial structure of production that developed in the post-Soviet period: the impact of structural shifts in the spatial distribution of production on the efficiency indicators for the use of the most important economic resources, labor and capital, as well as changes in some qualitative characteristics of space.

Labor productivity and capital productivity are among the generally accepted private performance indicators. For spatial systems that are complex socioeconomic formations, improvement of space characteristics (social, environmental, innovative, political), even unaccompanied by an increase in economic efficiency, can be regarded as a sign of rationalization of spatial organization and improvement in the quality of the economic space. A common method for studying qualitative changes in the spatial structure is to construct different kinds of ratings. This approach is also used to assess changes in the spatial organization of the economy.

For calculations, the author used decomposition into factors (Mikheeva 2014a; Spasskaya, 2003). The dynamics of indicators of resource use in the economy forms under the influence of two factors: changes in the regional structure of production and changes in the efficiency of their use in each region. As performance indicators, the author used the production of GRP in comparable prices per employee (labor productivity) and production of GRP per unit of fixed assets (capital productivity). The calculation methodology is described in detail in (Mikheeva, 2014b).

Labor productivity. Indicators of the dynamics of labor productivity at the national level are calculated by Rosstat,Footnote 10 which determines labor productivity indices as the quotient of dividing the indices of the physical volume of GDP (by TEA, indices of the physical volume of output) and indices of changes in total labor costs. Total labor costs are calculated based on labor costs for all types of work, including additional work and production for own consumption, normalized to provisional full-time equivalent workers. The source of information is the results of sample surveys of employment, the data of which are available for individual years.

For regions, indicators of the labor productivity dynamics are calculated in a similar way, but the GRP indicator is used as the reference. Information on total labor costs calculated according to the Rosstat methodology is available only for individual years, and the data for all years are not comparable. The indices of the physical volume of GVA in the regions necessary for calculations have been published by Rosstat only since 1997.

We saw our task in the analysis of the maximum possible period, therefore, comparable productivity indicators in the regions were calculated as the ratio of the produced (registered) GVA in comparable prices to the average annual number of people employed in the economy, such an indicator would be more correctly called the generation of GVA per one employed in the economy , in contrast to the indicator “labor productivity” defined above.

Comparison of the regional total output per employee and labor productivity for the economy as a whole, the dynamics of which are published by Rosstat, shows that the calculation results differ slightly: when calculated by the sum of regions, the indicators are in most cases higher than when calculating for the economy as a whole; this can be explained by more fully taking into account the total labor costs in the Rosstat calculations.

Labor productivity growth rates were calculated for 1997–2016 for the 79 federal subjects mentioned above. Labor productivity on average in the Russian Federation increased by 1.9 times in 2016 compared to 1996. In half the regions (41), productivity growth rates were higher than the national average.

The regions leading in productivity growth rates are represented by three groups: regions with a low base level of productivity, against which the productivity growth rates significantly exceeded the Russian average (the republics of Dagestan and Adygea); resource-producing regions, where new large capacities were introduced, which ensured a significant increase in production (Sakhalin oblast, Chukotka Autonomous Okrug); regions in which the manufacturing predominates in the production structure, the growth of labor productivity in which is due to modernization and the creation of new industries (Tambov, Belgorod, Rostov, and Leningrad oblasts, the Republic of Mordovia, and a number of other regions).

The contribution of regional factors and shifts in the spatial structure to the growth of labor productivity in the Russian economy as a whole is presented in Table 5. The calculation results show that during the period under review, changes in the spatial structure of production had almost no effect on the dynamics of labor productivity in the economy as a whole, the dominant factor determining it was the growth in labor productivity in individual regions. The influence of the regional and structural factors in most cases was unidirectional: when labor productivity grew, the contribution of both factors was positive.

Table 5. Increase in labor productivity in economy of Russian Federation due to regional and structural factors, %
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An exception was 2007, when changes in the spatial structure made an insignificant but nevertheless negative contribution to the overall dynamics of productivity. A drop in labor productivity took place during crisis-related years: in 1998 and 2009, the contribution of both factors was negative. The year 2015 stands out from the general pattern, when the contributions of regional factors and spatial structure proved multidirectional. However, this effect is statistical; employment data for 2015–2016 were calculated according to a new methodology that more fully takes into account the number of employees, organizations, and individual entrepreneurs not covered by statistical observation, as a result of which the average annual number of employees in most federal subjects increased.

On the whole, shifts in the distribution of production mainly affected the growth of labor productivity in regions, but their scale was insufficient to change the interregional proportions of the distribution of production. The low degree of influence of the spatial structure on the growth of labor productivity is because the share of each individual federal subject in the aggregate GVA indicator, except for a small number of large regions, is within 1–2%, so changes in their shares are insignificant from the viewpoint of the dynamics of the spatial structure as a whole.

Another condition that governed the dominance of intraregional factors was the almost universal reduction or slight increase in the number of people employed in the economy, in connection with which the growth in production in most regions was due to labor productivity.

The GRP indicator per employed person characterizes the value added of all TEA, including social services, the growth of labor productivity in which is a very conditional indicator. In the public sector, growth in output realistically characterizes the growth in costs per employee, which is important in assessing the income of institutional units formed in the region, but it has nothing to do with the effectiveness of production of these services. A more specific characterization of real shifts in the spatial structure due to changes in location is provided by the indicator of labor productivity in industry. Calculation of this indicator is also associated with statistical difficulties in assessing aggregate employment and the dynamics and structure of industrial production, for which the transition from the OKONKh (All-Union Classifier of Types of Activity) to OKVED disrupted the comparability of time series.

The dynamics of production and labor productivity in industry was extremely unstable. The growth of industrial production during almost the entire period under review occurred in conditions of either a decrease or a stable number of employees: a significant increase in number was observed only in 2000 and 2001, followed by a decrease; in 2010–2011 the number did not change; in 2015–2016, the number of employed formally increased due to changes in accounting methods. Thus, the growth of employment in industry could not have been a significant factor in the growth of production: the latter was based on growth of labor productivity.

Estimates of the contribution of regional and spatial factors to changes in labor productivity in industry were calculated in the same way as for GVA (Table 6); data for 1999–2003 were presented in accordance with OKONKh; for 2004–2016, in accordance with OKVED-2007—i.e., the absolute values of indicators for the two indicated periods are not comparable, but they can be used to characterize trends.

Table 6.   Increase in labor productivity in industry of Russian Federation due to regional and structural factors, %
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The growth of labor productivity in certain regions also proved dominant for the dynamics of the Russian indicator: the contribution of the spatial structure of industrial production was immeasurably less, but in most cases it was positive; i.e., industrial production shifted to regions with a higher level of labor productivity, although this process did not have a significant impact on the increase in the efficiency of the use of labor resources for the country as a whole.

The efficiency of fixed capital use is characterized by capital productivity indicators, which can serve as a good tool for assessing spatial shifts in the presence of correct statistical data.

Regional statistics publish data on fixed assets at full reported value; indicators of the dynamics of fixed assets in comparable prices are published only for Russia as a whole, and calculation of deflators shows their significant instability (Mikheeva, 2014b). Rosstat data show that the volume of fixed assets in comparable prices remained almost unchanged until 2005; the commissioning of new fixed assets ensured only compensation for their disposal. The dynamics of regional indicators of fixed assets in comparable prices was estimated from the average Russian deflators; these data were used in calculating the return on assets by region.

Indicators of return on assets as the efficiency of the use of fixed assets traditionally pertain to the main production assets directly involved in the production process. Share of fixed production assetsFootnote 11 in Russia as a whole in 2017 was 62.2%; their share in the federal subjects ranges from 27% in Moscow and 30–40% in the North Caucasus republics to 90–92% in the Khanty-Mansi, Yamalo-Nenets autonomous okrugs and Sakhalin oblast. There are no series of dynamics of fixed production and nonproduction assets; the total indicator of fixed assets in the economy characterizes the efficiency of using fixed assets in regions as an element of national wealth in the process of production and vital activity in the region; it can be used to describe the efficiency of the region’s economy as a whole.

The spatial distribution of fixed assets changed due to the commissioning of new funds and disposal of old ones. The total commissioning of fixed assets includes the commissioning of new production assets, which is the result of new construction and modernization of production, as well as new funds in the housing and infrastructure sectors. More than 45% of fixed assets for the period under review were introduced in the Central and Ural FDs; the absolute leaders in terms of the volume of fixed assets put into operation were Moscow, the Khanty-Mansi Autonomous Okrug, Moscow oblast, and the Yamalo-Nenets Autonomous Okrug. In the case of Moscow, significant volumes of new funds are not associated with the placement of new industries; this is mainly housing and transport construction.

The indicators of return on assets are objectively highly differentiated by region and are determined, in addition to the ratio of production and nonproduction assets mentioned above, by the sectoral structure of production in a region—the share of the most capital-intensive extractive and infrastructure industries. In regions with predominant manufacturing industries, the return on assets is higher; the growth in the share of extractive industries and infrastructural development of the territory leads, as a rule, to a decrease in the total return on fixed assets.

The influence of regional factors and shifts in the spatial structure of funds on the aggregate indicators of GVA production per ruble of fixed assets was assessed by the above method. As in the case of labor productivity, it is assumed that the change in the overall return on assets in the economy occurred under the influence of regional factors that determined the change in efficiency of the use of fixed assets in federal subjects and changes in the spatial structure of fixed assets.

Growth of the economy in the period under review took place with little changing volumes of funds; in this regard, formally, it should have been accompanied by growing efficiency of their use. Obviously, both the increase in efficiency of the use of fixed assets and growth in the utilization of existing capacities, which was a typical characteristic of recovery growth, could serve as a factor in the growth of production. Until the 2014–2016 crisis, the possibilities for increasing capacity utilization were assessed as low; therefore, economic growth was most likely accompanied by an increase in the efficiency of the use of fixed assets.

Estimation of the contribution of intraregional factors and factors of the spatial structure (Table 7) shows that, in contrast to labor productivity, spatial factors had a more significant impact on capital productivity; in some years (1997–1998, 2003, 2007–2008), regional factors accounted for about a third of the total change in capital productivity. In all years under consideration, the contribution of the spatial structure was positive; the spatial structure factor had a stabilizing effect on the decline in capital productivity in 1998, 2009, 2015 due to the drop in GRP with increasing volumes of fixed assets.

Table 7.   Increase in capital productivity in economy of Russian Federation due to regional and structural factors, %
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The estimates obtained for the return on fixed assets indicate that the proportions of employment and GVA production changed due to an increase in the share in both indicators of regions with a higher level of return on fixed assets. Under the assumptions adopted in the assessment, this means that the ongoing spatial shifts in the structure of fixed assets are aimed at increasing the efficiency of the economy.

Quality of life criteria. The above approaches to assessing changes in the spatial structure are based on the use of economic efficiency criteria. With a broader view of the problem, the end result of placement of new industries and the resulting spatial structural shifts is a change in the quality of the economic space. The latter also occurs both under the influence of placement of new industries and many other factors; to evaluate it, one can use other less formalized criteria related to certain aspects of spatial development. Let us now consider one of the criteria characterizing the quality of the economic space: change in quality of life. In assessing the impact of structural shifts in production on changes in the quality of the economic space, we assume that it improves (or the location of production becomes more rational) if the share of regions with high indicators of quality of life increases in the dynamics in the structure of production and employment.

In the literature, much attention has been devoted to problems of measuring the quality of life; in the international practice of comparing the level and quality of life, human development indices (HDI) are widely used (Doklad …, 2017; Doklad …, 2018; Grishina et al., 2012; Ivanov and Sagradov, 2001; Report …, 2004; Rimashevskaya, 2001; Soboleva, 2007; Valentey and Nesterov, 1999).

The HDI dynamics by Russian regions is presented in United Nations Development Program (UNDP) reports. The relative positions of regions in HDI ranking vary slightly from year to year: the HDI indicators for 1994 were used in the calculation—the first postreform year for which there are official data on GRP and HDI estimated with their use, and the latest publication of the HDI based on data for 2016 (Doklad …, 2018). Since series of comparable HDI indicators are needed to analyze the effectiveness of shifts in the location of production, the indices presented in these reports were used.

The absolute HDI values by regions are quite close; in dynamics, differentiation of regions by HDI is decreasing; in 1994, the ratio of the maximum value to the minimum was 1.29 times (Tyumen oblast and the Tyva Republic); in 2016, 1.21 times (Moscow and the Tyva Republic).

Analysis of the ratio of changes in the HDI and share of the respective region in total employment in the Russian Federation shows a weak positive relationship; i.e., in the structure of employment, the share of regions in which HDI indicators grew demonstrated a faster increase, with a correlation coefficient of 0.195. The trend is too weak to state that the shifts in employment that have taken place led to improvement in the quality of the economic space, but at least it cannot be argued that there is a reverse trend.

Similar relationships are also formed for change in the structure of value added in 2016 compared to 1994, although the cost proportions of production are more flexible than the proportions of the spatial distribution of employment.

A significant “event” characterizing the change in the spatial structure of GVA over the period under review was a sharp increase in the share of Moscow (by 10.6 percentage points in 2016 compared to 1994) and the share of Tyumen oblast (by 2.5 percentage points). It was associated with the growth and redistribution of revenues from oil and gas exports and was only partly determined by real shifts in the structure of production, but against this background, less significant changes are indistinguishable. If we exclude Moscow and Tyumen oblast from the calculation, then the relationship between the change in HDI and share of a region in created GVA is more pronounced, and the correlation coefficient increases from 0.229 to 0.324; i.e., the relationship is observed between growth of the HDI indicator and change in the relative positions of a region in production over a sufficiently long period, and this dependence can be characterized as the above-determined improvement in quality of the economic space as a result of shifts in the structure of production.

An inevitable consequence of shifts in the location of production is a change in territorial proportions, as well as in the place and role that regions occupy in the new conditions. The directions of such changes are generally expected and quite predictable; their general trend is a shift of economic activity to more favorable conditions for life and production.

The economic efficiency of the ongoing spatial shifts can be assessed by their contribution to the growth of general indicators of the efficiency of resource use: labor productivity and return on assets.

Analysis of the dynamics of labor productivity and return on fixed assets for 1996–2016 shows that the contribution of the spatial factor to the total increase in labor productivity and return on assets was incommensurably less than the contribution of the regional factors themselves. In other words, the overall growth in labor productivity and capital productivity was ensured by the growth of regional performance indicators, not by the relocation of production and labor resources to regions with higher efficiency indicators. The shifts that have taken place in the distribution of production and employment have not become a significant factor in the growth in the efficiency of resource use for the economy as a whole.

Analysis of changes in individual qualitative characteristics of the economic space due to ongoing structural shifts shows that the hypothesis about transfer of production and employment to regions characterized by higher quality of life indicators is confirmed only for spatial shifts in the structure of production. There is no obvious relationship between changes in the spatial structure of employment and indicators of the quality of life.

The methodological and statistical limitations of the analysis were mentioned above; nevertheless, the results indicate that changes in the spatial framework of the economy proved a weak factor in the growth of aggregate efficiency. The reason for this, in addition to the high stability of the spatial structure of production and employment, is the low mobility of production factors, primarily the population and labor resources, as well as, in part, state policy to support production and employment, which is indirectly related to the conditions of employment in regions, including quality of life and the level of labor productivity.

SPATIAL ASPECTS OF ECONOMIC MODERNIZATION

Directions of modernization. Modernization, or the effective use of modern advancements, covers all aspects of public life. In the economic and social sectors, a number of key areas of modernization stand out (Aganbegyan, 2010a, 2010b, 2011a, 2011b, 2011c, 2016):

— privatization of state property that can be used for commercial purposes;

— a significant increase in the share of medium and, in particular, small businesses in the economy;

— modernization of the country’s financial system, including budget reform and changes in the tax system towards reducing the tax burden on businesses and increasing the burden on households while increasing wages and incomes for low-income families;

— the formation of large sources of “long” money via a transition to funded pensions and the comprehensive development of insurance and mutual funds;

— a significant reduction in, and possibly zeroing of, the reserves of long-term liabilities of Russian banks;

— bankization of the country with an increase in the share of bank assets in GDP at least to the lowest level of European countries—up to 150 instead of 70% at present;

— transformation of regional administration with enlargement of administrative-territorial division and transfer of regions, mainly to self-sufficiency;

— modernization of the social sphere, including healthcare and housing and communal services.

Of course, this list does not cover all areas of modernization; obviously, the entire system of justice and law and other systems need modernization. The foundation of modernization is modernization of the real sector, which contains two interrelated areas: modernization of the production and technical base of the economy and modernization of the production structure, including diversification of exports.

A radical technological upgrade of production will make it possible to raise the productivity of social labor in the next 7–10 years by 2–3 times; reduce by half the energy intensity of GDP and 1.5 times, the material intensity; and radically improve the quality of products and create material conditions for the production of new, in total, innovative goods and services, increasing their share in the total sales volume.

Restructuring of the economy will increase the share of finished products with high added value, thereby reducing the share of fuel and raw materials industries and production of semifinished products. Owing to a technological upgrade of production, the output of final products per ton of oil and gas produced can be increased 2.5 times; per cubic meter of harvested wood, 3 times; per ton of smelted metal, 2 times. In the structure of exports, the share of fuel, raw materials, and semifinished products could be reduced from 85 to 40%, while the share of finished and innovative products and services could be increased to 60%.

The share of the knowledge economy (science, education, healthcare, information, and biotechnology) should increase in the structure of GDP, and the country’s innovative development will become the main source of economic growth. Whereas, at present, in terms of productivity of social labor and overall efficiency, product quality, progressiveness of the structure of the national economy, and the level of innovative development, Russia occupies 60th–80th place among 200 countries, as a result of modernization, it may approach the most developed countries of the world.

Technological upgrade. Fixed capital—the material foundation of social development—is hopelessly outdated in Russia. The depreciation of fixed assets in Russia is approaching 50%, and depreciation of machinery and equipment has already exceeded this critical limit. The average service life of machines and equipment is about 18 years, while machines more than 10 years old are considered old. The average service life of machinery and equipment in advanced countries is about 8 years.

Among the branches of the national economy, ferrous metallurgy, the pipe industry, a number of branches of the food industry, communications, and partly electrical engineering have been modernized; healthcare is currently doing so. All this is about 10% of the national economy, and the vast majority of it is extremely backward technically. This is especially true for energy, light industry, heavy engineering, machine tool building, oil refining, and many branches of chemistry, railway transport, pipeline systems, and housing and communal services.

It is necessary in the coming years to radically technically upgrade all major industries. This will have a significant effect if the country bets on the highest technologies and best machinery and equipment. Another important condition is special focus on training highly qualified personnel capable of effectively using this new equipment and technology. According to a rough estimate, such modernization will require about RUB 2 trln of additional investment annually. The average rate of return can be 5–7 years.

Modernization of the socioeconomic system requires a targeted and effective socioeconomic policy covering all of these areas; however, to modernize the real sector of the economy, regional economic policy is of particular importance, taking into account the entire diversity of spatial conditions and characteristics of Russia, including regional features of technological upgrade of the production apparatus, the specifics of the production structure of the economies of individual regions, and the spatial distribution of production. These peculiarities are very significant.

Regional peculiarities determine the initial conditions for modernization: the state and structure of the production apparatus in regions, the sectoral structure of production, including industry, as well as susceptibility of regions to innovation.

Priorities of regional economic policy. The tool ensuring modernization of the spatial structure of the economy is regional policy. Before defining regional policy, which, in our opinion, corresponds to solving the problems involved in modernizing the spatial structure, let us make a number of general remarks.

All countries with a large territory and significant level of spatial differentiation of the conditions of production and life of the population inevitably face the problem of choosing regional development priorities. The latter are determined depending on the regional policy model adopted in a particular country. Theoretical concepts define two types of regional policy: equalizing and incentivizing; however, in reality, some combination thereof is always implemented, including both elements of equalizing conditions for production and life, levels of economic development of regions, household income, and elements for incentivizing production in lagging regions.

How important can the influence of the chosen regional development priorities on general economic dynamics be is demonstrated by the example of European Union countries, China, and Russia, which, despite the significant differences in their economies, have significant spatial diversity of production and life conditions.

The EU’s supranational regional policy and China’s regional policy are, in a certain sense, opposites in terms of the chosen policy model and corresponding priorities. The EU’s regional policy (cohesion) was initially positioned as a policy to smooth out interregional differences, at least at the level of member countries, although referral to European regional policy as a “leveling” type is a hefty conditionality. Beginning with European integration and, in particular, with each subsequent enlargement of the EU, there have been significant differences in the levels of economic development between member countries and regions. The first European integration models considered regional policy among other things, as a mechanism to help regions achieve average indicators for the EU (although this aspect remains significant even now). With each subsequent stage of development of the EU, regional policy improved and became more complex, encompassing increasing new areas of socioeconomic development of the countries and regions of the EU. Currently, it is a policy that focuses on future opportunities, mobilizing underutilized potential, rather than paying compensation for the problems of the past.Footnote 12

There are three main directions in modern European regional policy.Footnote 13 First, there is emphasis on promoting economic convergence of countries and regions in order to help less developed regions (located mainly in new member countries) to catch up with the more developed ones. Second, measures are being taken to improve regional competitiveness and employment. The third priority is to promote cooperation between regions and countries in order to reduce the economic importance of national borders.

Allocations from structural funds created to implement the regional policy are distributed in accordance with 11 identified priorities: innovation and R&D, information and communication technologies, support for small and medium-sized businesses, a low-carbon economy, adaptation to climate change, environmental activity, network infrastructure, employment, social inclusion, education, and quality of governance.

For the policy that implements the EU’s financial plan for 2014–2020, from among these priorities, four key ones have been identified: support for R&D, information and communication technologies, small businesses. and a low-carbon economy.

The effectiveness of EU regional policy and its impact on economic growth are assessed differently (Regional’naya …, 2015; Synthesis …, 2016). From the viewpoint of increasing private indicators (building roads and other infrastructure facilities, providing employment, attracting investment to the weakest economies), positive shifts have been observed. Regarding the extent to which cohesion policy has achieved the long-term goal of equalizing interterritorial differences in the level of economic development, the answer is not so clear.

Positive trends in the development of EU countries observed until 2007, assessed as a result of ongoing supranational and national regional policies, were interrupted by the economic crisis.Footnote 14 Since 2008, household income has decreased, the unemployment rate has exceeded the indicators of the previous 20 years, interregional differentiation of unemployment rates has increased, and interregional differences in average per capita GRP indicators have either increased or stopped decreasing. As economic growth began to recover, these differences began to narrow again as a result of higher growth rates in countries with lower levels of GDP per capita.Footnote 15 The process of narrowing intracountry disparities was very slow and almost ceased after 2008 (Kramar, 2016). The use of formal methods for assessing the results of interregional equalization does not show a steady trend towards equalization (Becker et al., 2010; Cappelen et al., 2003; Pellegrini et al., 2016): in new countries, differentiation between central and peripheral regions is increasing, and the main factor determining interregional differentiation is the general economic dynamics. The current trend in EU regional policy is increased attention towards measures on incentivizing endogenous growth factors in each region.

A fundamentally important point of EU regional policy is that its priorities are specific and set for a certain period, not just declared. In most cases, targets are set, their implementation is given appropriate funding, and the results are regularly monitored.

Whereas the regional policy trend in the EU is a shift from a policy of equalization to incentivizing economic growth, the opposite example is China’s regional policy of China, where over a relatively short period of time of about 30 years there has been a change in priorities from supporting the most prosperous and promising regions to supporting problem areas and thus bringing the levels of economic development of the entire territory closer together

Regional policy in China, as well as market reforms, is carried out in stages; regions are included in the system of global economic relations at different rates and scales. In the 1980s, China adopted this concept, characterized as center–periphery (Samburova, 2010) and, in fact, is an incentivizing policy model. At the first stage, priority was given to the most promising regions (center), which had comparative competitive advantages in the global market. At the second stage, after promising regions achieve the planned economic indicators, support of lagging regions (periphery) commences.

It has been suggested that the eastern provinces would become the locomotive for economic development of the rest of China. Owing to this policy, eastern China has achieved outstanding economic success. However, its impact on the economic development of the rest of China remains limited: economic differentiation has even intensified during the economic reforms (Keidel, 2007). Now China has moved on to implementing the second stage of regional policy: in order to mitigate interregional differentiation, support is beginning to be provided to the lagging regions of central, northeast, and western China. As in the case of eastern China, the focus is on public investment in infrastructure projects and introduction of most favored nation status.

Analysis of long-term trends in regional development of the Chinese economy (Liao and Wei, 2016) shows that over the past 60 years, multiple peaks of regional inequality have been found, which coincides with various stages of China’s development strategies, but the trend towards regional inequality does not show clear divergent, convergent, or inverted U models. Regional inequality is sensitive to the scale of regions. While inequality between provinces shows a decreasing trend, interregional inequality between the eastern, western, and central regions continues to grow. However, both interregional and interprovincial disparities have declined significantly since the 2008 global economic crisis.

In general, the results of the new stage of regional policy have been assessed as positive (Samburova and Mironenko, 2016), and the difference between the level of economic development of different parts of China is decreasing, albeit slowly. In 2001, per capita GRP in the western region was 38.7% of GRP in the eastern, in 2014 the ratio was already 55.9%, and the minimum level of per capita GRP in 2014 was 25.1% of the maximum level.Footnote 16 However, most researchers have not been able to find strong influence of the development programs of the central and western regions on narrowing the gap between coastal (rich) and inland (poor) regions (Chen, 2010; Chen and Groenewold, 2010). The Go West program has shown some positive externalities towards development of the western provinces, but the overall results are still inconclusive (Li and Wei, 2010).

The economic programs of the Russian Federation Government included sections on regional policy since 1992. The goals and objectives presented therein have reflected the nationwide priorities of spatial development, such as strengthening of the economic unity of the country, raising the level and quality of life of the population, ensuring approximately equal conditions for social development in all regions, and the use of regional factors and favorable conditions for the formation of an effective socially oriented economy in the regions.

The Basic Provisions of Regional Policy in the Russian Federation (Osnovnye …, 1996), which were in effect until 2017, became the official document sha** state policy on the development of regions. The policies had such goals as ensuring uniform minimum social standards and equal social protection, guaranteeing the social rights of citizens established the Constitution of the Russian Federation, regardless of the economic opportunities of the regions, leveling the conditions for socioeconomic development of regions, and priority development of regions of particular strategic importance. Incentivizing the development of regions and cities with large scientific and technical potential and could become locomotives and growth points of the economy of the federal subjects was listed among the regional economic policy goals, but no specific incentive mechanisms were presented.

A concrete step in implementing state regional policy was the adoption in 2002 of the federal target program (FTP) Reducing Differences in the Socioeconomic Development of Regions of the Russian Federation (for 2002–2010 and until 2015).Footnote 17 The goal of the program was to reduce differences in the level of socioeconomic development of regions, to reduce the gap in the main indicators of socioeconomic development between the most developed and lagging regions by 1.5 times by 2010, and by 2 times by 2015.

The program to reduce interregional differences suffered the fate of the majority of FTPs adopted in the early 2000s. The lack of necessary funds in regional budgets also prevented fulfillment of regions’ obligations to cofinance the activities of the program. In 2006, funding of the program was terminated and implementation completed.

After the adoption in 2014 of the federal law FZ‑172 On Strategic Planning in the Russian Federation, a number of new documents appeared that define the national priorities of regional development, intended to ensure solution of economic growth challenges facing the country in modern conditions.

In 2017, the Fundamentals of the State Policy of Regional Development of the Russian Federation for the Period up to 2025,Footnote 18 Economic Security Strategy of the Russian Federation for the Period up to 2030Footnote 19 were adopted; in 2019, the Strategy for Spatial Development of the Russian Federation.Footnote 20

The Fundamentals of State Policy defines regional development priorities, including reducing differences in the level and quality of life of citizens of the Russian Federation living in different regions, as well as in cities and rural areas; reducing differences in the level of socioeconomic development of regions; achieving the required level of provision of infrastructure for all populated areas of the Russian Federation; and further development of the urbanization process, in particular, development of large urban agglomerations.

The same priorities are duplicated, with some variations, in the Strategy for Economic Security and Strategy for Spatial Development, highlighting priority development of the economic potential of Eastern Siberia, the Far North, the Far East, the North Caucasus, Crimea, and Kaliningrad oblast; development of the Northern Sea Route; and modernization of the Baikal–Amur and Trans-Siberian railways.

The list of regional development priorities in the new documents has not undergone significant changes; an equalizing regional policy is still declared, but no specific measures for its implementation, similar to the 2002 Program, are proposed. The priority remains development of regions of geostrategic importance.

The Spatial Development Strategy envisages the following priorities for the spatial development of Russia for the period up to 2025: “advance development of territories with a low level of socioeconomic development, which have their own potential for economic growth, as well as territories with a low population density and predictable increase in economic potential; development of promising centers of economic growth with an increase in their number and maximum dispersal throughout the territory of the Russian Federation; social provision of territories with low population density with insufficient own potential for economic growth.”

Relatively new are the priorities of regional policy regarding its incentivizing part, special support for points of growth and centers of growth, which (in different formulations) define the further development of the urbanization process, in particular, the development of large urban agglomerations.

The question of how this priority can become a significant factor in the modernization and growth of the Russian economy remains open. The widespread idea (Dmitriev, 2016; Scale …, 2017) that economic growth is concentrated in large cities and urban agglomerations has no convincing scientific justification; at least in the Russian case, the situation is not unambiguous.

Analysis of foreign literature sources and urban economics (Melnikova, 2017) shows that there are completely different models of urban systems in countries with similar living standards. Theses on the superiority of large cities in terms of production efficiency and economic growth rates have been tested on extensive statistical material and have not been unambiguously confirmed. The development of the urbanization process will contribute to growth of the national economy only if it is not reduced to concentration of resources and population in urban agglomerations, but rather ensures revealing the potential of each city, regardless of size.

Analysis of the dynamics of major cities (Kuznetsova, 2017) shows that concentration of the population in million-plus cities continues to increase, but the situation with a change in their role in manufacturing and retail trade of regions is develo** differently. The number of people employed in manufacturing and the share of processing in employment is declining almost everywhere; this is perhaps a consequence of the transformation and modernization of the industry itself and the increase in the share of technologically complex industries. However, in the future, the role of large cities as industrial centers will increase only if high-tech industry develops in the country. A serious problem for the development of million-plus cities is development of the social sphere.

The spatial resource for the growth of economic efficiency in Russia is associated not with an increase in the level of urbanization, but with changes in the internal structure of the urban system and its diversity (Kolomak, 2016); i.e., the development of cities of different sizes has a positive impact on the level of overall regional productivity.

The priority for development of urban agglomerations outlined in official documents is only a declaration so far, since not only specific objects of state support and the mechanisms intended for this, but also the urban agglomerations themselves as objects of management, have not been defined. Therefore, the question of whether they can become new points of growth is still open.

Regional policy aimed at modernization. Today for countries with a market economy, there are three different approaches to regulating the proportions of spatial growth and eliminating disproportions (Rebalancing …, 2011).

The World Bank’s position is that regulation should aim to smooth out spatial differences in quality of life, not spatial differences in business activity. The basis of economic policy in this approach should be efforts to develop the economic integration of regions, using all tools at hand: institutions and infrastructure, which improve communication opportunities, access to markets, incentives contributing to migration of the population to more dynamic regions. Regional policy that implements this approach involves, in particular, incentivizing the activity of private business in leading cities to ensure realization of agglomeration effects.

The second approach is implemented by the EU and OECD (Barca, 2009; OECD …, 2006); it is based on the fact that spatial policy should be aimed at growth of all regions, in order to maximize the use of their potential. Modern ideas about regional policy proposed by the OECD (Green Growth …, 2010; OECD …, 2006) highlight as key imperatives the creation of wealth in all regions, the role of innovation, and modernization of regional assets. This approach does not contradict concentration of resources to support growth in certain areas, including areas with a high level of economic activity, if they have the appropriate assets, the efficiency of which can be enhanced by agglomeration effects. Implicitly, this approach implies smoothing of differentiation of business activity indicators in regions; i.e., we are talking about regional growth (and its effectiveness within a region), not regional redistribution, which was a key argument in regional policy of the 1980s.

The third approach is based on the fact that the subject of regional policy is solution of spatial problems of employment; in this regard, interregional differentiation and spatial disproportions are considered precisely from the viewpoint of employment (the position of the UK Labor and Employment Commission). It is believed that the public sector plays a key role in ensuring the balance of spatial development. Since in certain regions the market cannot provide economic growth generated by the private sector, the function of creating jobs (i.e., jobs in the public sector) falls on the public sector in lagging regions in order to utilize unused labor resources (Coutts et al., 2007; Green, 2009).

The state’s intended role in ensuring the balance of regional development differs significantly depending on the approach used. The World Bank actually leaves the economic development of regions at the mercy of the market, considering the goal of regional policy to smooth out interregional differences in living standards, which, with this logic, can occur due to interregional redistribution of resources and with active support for movement of population and production to centers of economic growth. The approach taken in the EU and OECD suggests that growth should be a goal in all regions; moreover, it can be concentrated on places or assets with the greatest potential.

The latter approach assumes the state’s active role in regulating spatial proportions through development of the public sector, which should be developed in places where the market does not create enough jobs.

The first position is ideologically close to the concept of polarized spatial development, and the second, to the concept of endogenous economic growth, oriented towards efficient use of the resources and opportunities of each region.

The topic of formation of new regional policy aimed at modernizing the Russian economy is being actively discussed both in scientific literature and in practice. The emphasis is either on active state modernization policy and mobilization of significant public resources, or on creating institutional conditions and maintaining market incentives for the modernization of production and innovation.

The Concept of Long-Term Development of the Russian Federation for the Period up to 2020,Footnote 21 adopted in 2008 and by now long forgotten, in which the transition to an innovative development model was declared as the goal of Russia’s long-term development, presumed that would take place on the basis of active state intervention. The innovative model assumed transition to a new type of spatial development of the Russian economy, including, among other things, incentivizing the formation of new centers of socioeconomic development based on development of energy and transport infrastructure and creating a network of territorial production clusters that realize the competitive potential of territories; coordinating state infrastructure investments and business investment strategies in regions, taking into account spatial development priorities and resource constraints, including demographic ones; reducing differentiation in the level and quality of life of the population in regions through effective social and budgetary policy mechanisms.

Later, ideas of active state regulation of modernization of spatial development processes were expressed in the so-called project approach, the advantages of which were actively promoted in the scientific literature (Fetisov, 2011; Glaz’ev et al., 2011; Kuleshov et al., 2009; Tatarkin, 2011). The essence of this approach, already in the form of official doctrine, was outlined in 2011 by then President of the Russian Federation D.A. Medvedev, dedicated to enhancing the role of regions in the modernization of the country’s economy: “The essence of the program (project) approach is the formation of a single interconnected set of state and regional programs aimed at solving the most acute socioeconomic problems in priority areas and allocation of budgetary resources to regions during implementation of these programs, based on the principles of cofinancing, repayment, and focus on a specific result.”Footnote 22

The ideas of the project (program) approach to modernization of the regional economy were partly implemented in the development strategies of federal districts adopted by the Russian Federation Government, but the project approach itself often remains more of a wish than a real mechanism for spatial development. The central element of all the submitted documents are large investment projects, primarily infrastructure projects implemented by the state, as well as by large companies, but the specific sources of their financing, except for the cases of major national projects, such as the Sochi Olympics or the APEC forum, have not been determined, and no one bears responsibility for their implementation.

The issues of creating institutional conditions that incentivize modernization of spatial development are also widely discussed in the literature (see, e.g., Glaz’ev, 2015; Strukturno-investitsionnaya …, 2017; Vosstanovlenie …, 2016; World …, 2009). The main ideas of studies in this direction are as follows: the creation of a spatial frame of zones and points of advanced development capable of transmitting innovations to the vast periphery of the country, relying on modernization centers identified by competitive advantages, and reducing barriers to their development; systemic support of large cities as translators of innovations; development of mechanisms for horizontal integration of space; the use of the institutions of federalism as a mechanism for coordinating regional interests; use as tools to smooth out interregional differences in social policy and human development (Mikheeva, 2018).

Practical directions for creating institutional conditions for the modernization of spatial development, which in a certain sense can be considered an expression of the position of the Russian Federation Government regarding spatial modernization, are presented in the Report of the Working Group for the Session of the State Council (Misharin et al., 2011). The document systematizes and categorizes a number of ideas for regional policy that had already been expressed in various publications; in this regard, the report in concentrated form presents the concept of modernizing regional policy, the main provisions of which have already been accepted by the scientific community and are reflected in official documents.

The essence of the proposed new regional policy necessary for modernization of the economy is creation by the state of an institutional environment for modernization through various development institutions, as well as creation of a polycentric spatial structure of the economy, which is suggested to be formed by identifying and supporting the development of centers of economic growth. It is important that reliance on the processes of self-development is considered the main driving force for modernizing the spatial structure; this thesis is in line with modern trends in regional science, focusing on endogenous growth factors. This requires development of mechanisms and tools for selecting the most effective priorities for territorial development, including in relation to problem areas, aimed at improving their stable functioning.

One of the key economic regional policy tools is improvement and creation of new forms of spatial organization of the economy, in particular, business territories. The established typical examples of such territories, which have been tested in global and domestic practice and science, are technoparks, special economic zones, industrial parks, transport and logistics centers, specialized trade and warehouse zones, etc.

The implementation of the new regional policy is supposed to be based on the creation and improvement of the effectiveness of various development institutions. As well, institutions should be sufficiently diverse and pursue different territorial development goals: institutions that implement direct actions of the state to achieve the main regional policy provisions, including in relation to problem areas (housing and communal services reform fund, financial support fund for federal subjects, regional finance reform fund, regional development fund, etc.); institutions aimed at incentivizing the innovative development of territories (special economic zones, innovation cities, etc.); institutions oriented towards changing regional administrative technologies; institutions associated with incentivizing the business community in the form of strengthening horizontal ties, including cluster forms of business development.

The project approach is considered the basis for implementing regional policy and regional administration. Based on the strategic priorities of the regional policy, programs should be developed that cover regions' most complex problems.

Specific implementation of the project approach involves the system of national projects adopted for the period up to 2024. National goals and strategic objectives for the development of Russia for the Period up to 2024, formulated by the President of the Russian Federation, initially in his Address to the Federal Assembly on March 1, 2018, developed in Decree no. 204 of May 7, 2018 (hereinafter Decree 2018) and the Address to the Federal Assembly on February 20, 2019, are aimed at breakthrough scientific, technical, and socioeconomic development of the country; they cover all key areas of the economy, including structural restructuring of the economy and the social sphere. According to experts (Ivanter, 2019), the immediate goal of national projects is not economic growth, but a starting impetus to investment, business, and, as a result, growth of important macroeconomic indicators such as GDP. The implementation of national projects can improve the quality of life of the population, which manifests itself precisely at the regional level.

Despite the importance of creating an institutional environment for modernization of the economy, the question of the interregional distribution of limited resources for modernization remains open; i.e., by and large, what kind of regional policy is needed for the modernization of the economy: incentivizing or equalizing?

The answer to this question is not so simple. The regional policy pursued so far has had more of an equalizing character; moreover, the equalization concerned current budget expenditures, and only implementation of large infrastructure projects can be considered, in a certain sense, as equalizing the conditions for doing business.

The presented proposals for a new regional policy, in fact, involve its incentivizing type, the implementation of which, in order to achieve real results, will require a sharp change in the interregional proportions of the distribution of resources, which in principle may yield a significant and quick effect, but greatly increases the risks of underfunding and both negative social and economic consequences for underdeveloped and problematic regions not among the priorities.

The problem of modernizing the spatial structure of the economy, like any other problem related to spatial development, has two key aspects.

The first concerns reliance on the competitive advantages of individual regions, taking into account the characteristics of the production potential and structure when choosing priority areas for modernization and their comprehensive stimulation aimed at increasing the overall efficiency of the economy and achieving the maximum economic effect.

The second aspect stems from uneven spatial distribution of the population and production and is associated with smoothing of the negative externalities of the spatial unevenness of modernization and inevitable growth of interregional differentiation of household income and regional budgets, which will result from outstrip** growth in production and its efficiency in the most advanced regions and growth of social and economic problems in lagging regions. The latter is and will continue to happen regardless of whether the most advanced regions are additionally stimulated; however, the latter can substantially intensify the processes of interregional differentiation and aggravate its negative externalities, which are exacerbated by depopulation processes in most Russian regions and extremely low mobility of the population and labor resources.

The main way to reduce the unevenness of spatial development is to spur the development of backward (depressed and underdeveloped) regions, stimulate the inflow of capital into them, build new enterprises, create jobs with high wages, etc. However, lagging regions have, as a rule, a low level of provision of infrastructure, as well as a lower level of labor productivity and return on capital, so a policy of artificially attracting investments to lagging regions leads to a decrease in aggregate growth rates compared to attraction of resources to the most efficient regions. Such a policy results in, perhaps, a slower, but more uniform modernization of the spatial structure, obviates critical spatial disproportions, and is ultimately aimed at preserving the country’s territorial integrity.